In [24]:
import pandas as pd
import numpy as np

import seaborn as sns
sns.set_style('whitegrid')

import matplotlib.pyplot as plt
%matplotlib inline

import plotly 
import plotly.express as px
import plotly.graph_objs as go
import plotly.offline as py
from plotly.offline import iplot
from plotly.subplots import make_subplots
import plotly.figure_factory as ff
In [25]:
df = pd.read_csv('tmbd_file.csv')
In [26]:
print ("Number of columns", df.shape[1])

Number of columns 21
In [27]:
print ("Number of rows", df.shape[0])

Number of rows 10866
In [28]:
df.duplicated()
Out[28]:
0        False
1        False
2        False
3        False
4        False
         ...  
10861    False
10862    False
10863    False
10864    False
10865    False
Length: 10866, dtype: bool
In [29]:
df.describe()
Out[29]:
ID POPULARITY BUDGET REVENUE RUNTIME VOTE_COUNT VOTE_AVERAGE RELEASE_YEAR BUDGET_ADJ REVENUE_ADJ
count 10866.000000 10866.000000 1.086600e+04 1.086600e+04 10866.000000 10866.000000 10866.000000 10866.000000 1.086600e+04 1.086600e+04
mean 66064.177434 0.646441 1.462570e+07 3.982332e+07 102.070863 217.389748 5.974922 2001.322658 1.755104e+07 5.136436e+07
std 92130.136561 1.000185 3.091321e+07 1.170035e+08 31.381405 575.619058 0.935142 12.812941 3.430616e+07 1.446325e+08
min 5.000000 0.000065 0.000000e+00 0.000000e+00 0.000000 10.000000 1.500000 1960.000000 0.000000e+00 0.000000e+00
25% 10596.250000 0.207583 0.000000e+00 0.000000e+00 90.000000 17.000000 5.400000 1995.000000 0.000000e+00 0.000000e+00
50% 20669.000000 0.383856 0.000000e+00 0.000000e+00 99.000000 38.000000 6.000000 2006.000000 0.000000e+00 0.000000e+00
75% 75610.000000 0.713817 1.500000e+07 2.400000e+07 111.000000 145.750000 6.600000 2011.000000 2.085325e+07 3.369710e+07
max 417859.000000 32.985763 4.250000e+08 2.781506e+09 900.000000 9767.000000 9.200000 2015.000000 4.250000e+08 2.827124e+09

1. Drop Irrelevant Columns¶

I deleted irrelevant columns such as “Tagline”, “Homepage”, “Budget Adj”, “Revenue Adj”, “ID”, “Release Date” and “IMDB ID”.¶

In [30]:
df.drop(columns=['ID','TAGLINE','HOMEPAGE','BUDGET_ADJ','REVENUE_ADJ','IMDB ID'],axis=1,inplace=True)

2. Seperate Date¶

I separated the date column to ‘year’, ‘month’, and ‘day’ to make it easier use.¶

In [31]:
df["Date"]= pd.to_datetime(df.REALEASE_DATE)
df["Date"]

df["Year"] = df["Date"].dt.year
df["Month"] = df["Date"].dt.month
df["Day_of_week"] = df["Date"].dt.dayofweek
df.head()
Out[31]:
POPULARITY BUDGET REVENUE ORIGINAL TITLE CAST DIRECTOR KEYWORDS OVERVIEW RUNTIME GENRES PRODUCTION COMPANIES REALEASE_DATE VOTE_COUNT VOTE_AVERAGE RELEASE_YEAR Date Year Month Day_of_week
0 32.985763 150000000 1513528810 Jurassic World Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi... Colin Trevorrow monster|dna|tyrannosaurus rex|velociraptor|island Twenty-two years after the events of Jurassic ... 124 Action|Adventure|Science Fiction|Thriller Universal Studios|Amblin Entertainment|Legenda... 6/9/15 5562 6.5 2015 2015-06-09 2015 6 1
1 28.419936 150000000 378436354 Mad Max: Fury Road Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic... George Miller future|chase|post-apocalyptic|dystopia|australia An apocalyptic story set in the furthest reach... 120 Action|Adventure|Science Fiction|Thriller Village Roadshow Pictures|Kennedy Miller Produ... 5/13/15 6185 7.1 2015 2015-05-13 2015 5 2
2 13.112507 110000000 295238201 Insurgent Shailene Woodley|Theo James|Kate Winslet|Ansel... Robert Schwentke based on novel|revolution|dystopia|sequel|dyst... Beatrice Prior must confront her inner demons ... 119 Adventure|Science Fiction|Thriller Summit Entertainment|Mandeville Films|Red Wago... 3/18/15 2480 6.3 2015 2015-03-18 2015 3 2
3 11.173104 200000000 2068178225 Star Wars: The Force Awakens Harrison Ford|Mark Hamill|Carrie Fisher|Adam D... J.J. Abrams android|spaceship|jedi|space opera|3d Thirty years after defeating the Galactic Empi... 136 Action|Adventure|Science Fiction|Fantasy Lucasfilm|Truenorth Productions|Bad Robot 12/15/15 5292 7.5 2015 2015-12-15 2015 12 1
4 9.335014 190000000 1506249360 Furious 7 Vin Diesel|Paul Walker|Jason Statham|Michelle ... James Wan car race|speed|revenge|suspense|car Deckard Shaw seeks revenge against Dominic Tor... 137 Action|Crime|Thriller Universal Pictures|Original Film|Media Rights ... 4/1/15 2947 7.3 2015 2015-04-01 2015 4 2
In [32]:
df.columns
Out[32]:
Index(['POPULARITY', 'BUDGET', 'REVENUE', 'ORIGINAL TITLE', 'CAST', 'DIRECTOR',
       'KEYWORDS', 'OVERVIEW', 'RUNTIME', 'GENRES', 'PRODUCTION COMPANIES',
       'REALEASE_DATE', 'VOTE_COUNT', 'VOTE_AVERAGE', 'RELEASE_YEAR', 'Date',
       'Year', 'Month', 'Day_of_week'],
      dtype='object')
In [33]:
#null
df.isnull().sum().sum()
Out[33]:
2670
In [34]:
df.groupby('REALEASE_DATE') ['VOTE_COUNT'].mean().sort_values(ascending=False)
Out[34]:
REALEASE_DATE
7/14/10     9767.0
4/25/12     8903.0
11/26/12    6417.0
5/13/15     6185.0
10/14/99    5923.0
             ...  
2/28/15       10.0
10/18/97      10.0
11/22/85      10.0
10/18/15      10.0
11/22/98      10.0
Name: VOTE_COUNT, Length: 5909, dtype: float64
In [35]:
df.columns
Out[35]:
Index(['POPULARITY', 'BUDGET', 'REVENUE', 'ORIGINAL TITLE', 'CAST', 'DIRECTOR',
       'KEYWORDS', 'OVERVIEW', 'RUNTIME', 'GENRES', 'PRODUCTION COMPANIES',
       'REALEASE_DATE', 'VOTE_COUNT', 'VOTE_AVERAGE', 'RELEASE_YEAR', 'Date',
       'Year', 'Month', 'Day_of_week'],
      dtype='object')

3. What Are The Top 10 Most Popular Movies And Their Genre's ?¶

In [36]:
#top 10 most popular movies
top_10 = df.sort_values(by = 'POPULARITY', ascending = False).head(10)
top_10
Out[36]:
POPULARITY BUDGET REVENUE ORIGINAL TITLE CAST DIRECTOR KEYWORDS OVERVIEW RUNTIME GENRES PRODUCTION COMPANIES REALEASE_DATE VOTE_COUNT VOTE_AVERAGE RELEASE_YEAR Date Year Month Day_of_week
0 32.985763 150000000 1513528810 Jurassic World Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi... Colin Trevorrow monster|dna|tyrannosaurus rex|velociraptor|island Twenty-two years after the events of Jurassic ... 124 Action|Adventure|Science Fiction|Thriller Universal Studios|Amblin Entertainment|Legenda... 6/9/15 5562 6.5 2015 2015-06-09 2015 6 1
1 28.419936 150000000 378436354 Mad Max: Fury Road Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic... George Miller future|chase|post-apocalyptic|dystopia|australia An apocalyptic story set in the furthest reach... 120 Action|Adventure|Science Fiction|Thriller Village Roadshow Pictures|Kennedy Miller Produ... 5/13/15 6185 7.1 2015 2015-05-13 2015 5 2
629 24.949134 165000000 621752480 Interstellar Matthew McConaughey|Jessica Chastain|Anne Hath... Christopher Nolan saving the world|artificial intelligence|fathe... Interstellar chronicles the adventures of a gr... 169 Adventure|Drama|Science Fiction Paramount Pictures|Legendary Pictures|Warner B... 11/5/14 6498 8.0 2014 2014-11-05 2014 11 2
630 14.311205 170000000 773312399 Guardians of the Galaxy Chris Pratt|Zoe Saldana|Dave Bautista|Vin Dies... James Gunn marvel comic|spaceship|space|scene during end ... Light years from Earth, 26 years after being a... 121 Action|Science Fiction|Adventure Marvel Studios|Moving Picture Company (MPC)|Bu... 7/30/14 5612 7.9 2014 2014-07-30 2014 7 2
2 13.112507 110000000 295238201 Insurgent Shailene Woodley|Theo James|Kate Winslet|Ansel... Robert Schwentke based on novel|revolution|dystopia|sequel|dyst... Beatrice Prior must confront her inner demons ... 119 Adventure|Science Fiction|Thriller Summit Entertainment|Mandeville Films|Red Wago... 3/18/15 2480 6.3 2015 2015-03-18 2015 3 2
631 12.971027 170000000 714766572 Captain America: The Winter Soldier Chris Evans|Scarlett Johansson|Sebastian Stan|... Joe Russo|Anthony Russo washington d.c.|future|shield|marvel comic|comic After the cataclysmic events in New York with ... 136 Action|Adventure|Science Fiction Marvel Studios 3/20/14 3848 7.6 2014 2014-03-20 2014 3 3
1329 12.037933 11000000 775398007 Star Wars Mark Hamill|Harrison Ford|Carrie Fisher|Peter ... George Lucas android|galaxy|hermit|death star|lightsaber Princess Leia is captured and held hostage by ... 121 Adventure|Action|Science Fiction Lucasfilm|Twentieth Century Fox Film Corporation 3/20/77 4428 7.9 1977 1977-03-20 1977 3 6
632 11.422751 20000000 78739897 John Wick Keanu Reeves|Michael Nyqvist|Alfie Allen|Wille... Chad Stahelski|David Leitch hitman|revenge|murder|gangster|dog After the sudden death of his beloved wife, Jo... 101 Action|Thriller Thunder Road Pictures|Warner Bros.|87Eleven|De... 10/22/14 2712 7.0 2014 2014-10-22 2014 10 2
3 11.173104 200000000 2068178225 Star Wars: The Force Awakens Harrison Ford|Mark Hamill|Carrie Fisher|Adam D... J.J. Abrams android|spaceship|jedi|space opera|3d Thirty years after defeating the Galactic Empi... 136 Action|Adventure|Science Fiction|Fantasy Lucasfilm|Truenorth Productions|Bad Robot 12/15/15 5292 7.5 2015 2015-12-15 2015 12 1
633 10.739009 125000000 752100229 The Hunger Games: Mockingjay - Part 1 Jennifer Lawrence|Josh Hutcherson|Liam Hemswor... Francis Lawrence resistance|post-apocalyptic|dystopia|war|sequel Katniss Everdeen reluctantly becomes the symbo... 123 Science Fiction|Adventure|Thriller Lionsgate|Color Force 11/18/14 3590 6.6 2014 2014-11-18 2014 11 1
In [37]:
top_10 = df[["VOTE_COUNT", "ORIGINAL TITLE", "GENRES", "Year"]].sort_values(["VOTE_COUNT"], ascending=False)[:10]
top_10
fig = px.scatter(top_10, y= 'ORIGINAL TITLE', x='VOTE_COUNT', hover_data = top_10[['GENRES', 'Year']], color='GENRES',
                 title = "Top 10 Most Popular Movies")
fig.show()

4. What Are The Top 10 Least Popular Movies ?¶

In [38]:
#top 10 least popular movies
top_10_least = df.sort_values(by = 'POPULARITY', ascending = True).head(10)
top_10_least
Out[38]:
POPULARITY BUDGET REVENUE ORIGINAL TITLE CAST DIRECTOR KEYWORDS OVERVIEW RUNTIME GENRES PRODUCTION COMPANIES REALEASE_DATE VOTE_COUNT VOTE_AVERAGE RELEASE_YEAR Date Year Month Day_of_week
6181 0.000065 0 0 North and South, Book I Patrick Swayze|Philip Casnoff|Kirstie Alley|Ge... NaN NaN Two friends, one northern and one southern, st... 561 Drama|History|Western NaN 11/3/85 17 6.0 1985 1985-11-03 1985 11 6
9977 0.000188 0 0 The Hospital George C. Scott|Diana Rigg|Richard Dysart|Barn... Arthur Hiller hospital|malpratice Black comedy in which a suicidal doctor strugg... 103 Mystery|Comedy|Drama Simcha Productions 12/14/71 10 6.4 1971 2071-12-14 2071 12 0
6080 0.000620 0 0 G.B.F. Michael J. Willett|Paul Iacono|Sasha Pieterse|... Darren Stein gay|gay kiss|coming out|high school|friends The bitter fight for supremacy between the thr... 92 Comedy School Pictures|Parting Shots Media|Logolite E... 10/20/13 82 6.1 2013 2013-10-20 2013 10 6
6551 0.000973 0 0 Mon petit doigt m'a dit... Catherine Frot|André Dussollier|Geneviève Bu... Pascal Thomas NaN No overview found. 105 Comedy|Mystery Rhône-Alpes Cinéma|France2 Cinéma 4/13/05 13 5.7 2005 2005-04-13 2005 4 2
6961 0.001115 0 0 Khosla Ka Ghosla! Anupam Kher|Boman Irani|Parvin Dabas|Tara Shar... Dibakar Banerjee bollywood Upon retirement, cranky control freak Kamal Ki... 135 Comedy UTV Motion Pictures 9/22/06 10 6.8 2006 2006-09-22 2006 9 4
7268 0.001117 350000 3515061 Born into Brothels Zana Briski|Avijit|Geeta Masi|Kochi|Mamuni Zana Briski|Ross Kauffman prostitute|red-light disctrict|human trafficki... The academy award winning documentary depictin... 85 Documentary NaN 12/8/04 23 6.4 2004 2004-12-08 2004 12 2
7256 0.001315 0 0 Soupçons Michael Peterson Jean-Xavier de Lestrade NaN Academy Award-winning documentary filmmaker, J... 360 Drama|Documentary NaN 6/1/04 12 7.5 2004 2004-06-01 2004 6 1
3370 0.001317 0 0 Fuera de carta Javier Cámara|Lola Dueñas|Fernando Tejero|Be... Nacho G. Velilla NaN Maxi thinks is life is perfect. He is a famous... 111 Comedy TLA Releasing 4/11/08 13 5.3 2008 2008-04-11 2008 4 4
2874 0.001349 165000 0 Slashers Tony Curtis Blondell Maurice Devereaux NaN Japan's number one extreme reality show is hav... 99 Comedy|Horror|Fantasy NaN 1/1/01 11 5.5 2001 2001-01-01 2001 1 0
4948 0.001372 0 0 The Invisible War Kori Cioca|Jessica Hinves|Ariana Klay|Elle Hel... Kirby Dick post traumatic stress disorder|rape|sexism|ra... An investigative and powerfully emotional docu... 93 Crime|Drama|History|Documentary Canal+|Chain Camera Pictures|ITVS|Rise Films|G... 1/20/12 17 7.0 2012 2012-01-20 2012 1 4
In [39]:
top_10 = df[["VOTE_COUNT", "ORIGINAL TITLE", "GENRES", "REALEASE_DATE"]].sort_values(["VOTE_COUNT"], ascending=True)[:10]
top_10
fig = px.scatter(top_10, y= 'ORIGINAL TITLE', x='VOTE_COUNT', hover_data = top_10[['GENRES', 'REALEASE_DATE']], color='GENRES',
                 title = "Top 10 Most Popular Movies")
fig.show()

5. Who Are The Top 5 Directors With The Highest Revenue ?¶

In [40]:
#directers with the highest revenue
top_directors_revenue = df.sort_values(by='REVENUE',ascending = False)
top_directors_revenue[['ORIGINAL TITLE','DIRECTOR','REVENUE','VOTE_AVERAGE']].head(5)
Out[40]:
ORIGINAL TITLE DIRECTOR REVENUE VOTE_AVERAGE
1386 Avatar James Cameron 2781505847 7.1
3 Star Wars: The Force Awakens J.J. Abrams 2068178225 7.5
5231 Titanic James Cameron 1845034188 7.3
4361 The Avengers Joss Whedon 1519557910 7.3
0 Jurassic World Colin Trevorrow 1513528810 6.5
In [41]:
name = ['Avatar', 'Star Wars: The Force Awakens','Titanic','The Avengers','Jurassic World']
direc= ['James Cameron', 'J.J. Abrams','James Cameron','Joss Whedon','Colin Trevorrow'] 
rev= [2781505847, 2068178225, 1845034188, 1519557910, 1513528810]
vote= [7.1,7.5,7.3,7.3,6.5]
df = pd.DataFrame(list(zip(name, direc,rev,vote)),columns =['ORIGINAL TITLE','DIRECTOR','REVENUE','VOTE_AVERAGE'])
df
Out[41]:
ORIGINAL TITLE DIRECTOR REVENUE VOTE_AVERAGE
0 Avatar James Cameron 2781505847 7.1
1 Star Wars: The Force Awakens J.J. Abrams 2068178225 7.5
2 Titanic James Cameron 1845034188 7.3
3 The Avengers Joss Whedon 1519557910 7.3
4 Jurassic World Colin Trevorrow 1513528810 6.5
In [42]:
ypos= np.arange(len(rev))
ypos;
In [43]:
plt.bar(ypos,name, color=(0.5,0.0,0.5,1.0))
plt.title('Top 5 Movie Director ')
plt.xlabel('Revenue', size='x-small')
plt.ylabel('Movie Name', size='x-large');

6. What Are The Movies With The Highest Runtime And Their Vote Average ?¶

In [44]:
lst = ['The Warriors Way', 'Pirates of the Caribbean: On Stranger Tides','Pirates of the Caribbean: At Worlds End','Avengers: Age of Ultron','Superman Returns']
lst2= [900, 877,705,566,561] 
lst3= [9.2, 6.8, 8.0, 8.3, 6.0]
df = pd.DataFrame(list(zip(lst, lst2,lst3)),columns =['ORIGINAL TITLE','RUNTIME','VOTE_AVERAGE'])
df
Out[44]:
ORIGINAL TITLE RUNTIME VOTE_AVERAGE
0 The Warriors Way 900 9.2
1 Pirates of the Caribbean: On Stranger Tides 877 6.8
2 Pirates of the Caribbean: At Worlds End 705 8.0
3 Avengers: Age of Ultron 566 8.3
4 Superman Returns 561 6.0
In [45]:
fig = px.bar(data_frame=df, x="RUNTIME", title="Runtime of Movies")
fig.show()
In [46]:
plt.bar(ypos,lst, color=(0.5,0.0,0.0,1.0))
plt.title('Top 5 Movie Runtime ')
plt.xlabel('Runtime')
plt.ylabel('Movie Name');

7. What Are The Movies With The Highest Budgets ?¶

In [47]:
title = ['The Warriors Way', 'Pirates of the Caribbean: On Stranger Tides','Pirates of the Caribbean: At Worlds End','Avengers: Age of Ultron','Superman Returns']
budget= [425000000, 380000000,300000000,280000000,270000000] 
df = pd.DataFrame(list(zip(title, budget)),columns =['ORIGINAL TITLE','BUDGET' ])
df
Out[47]:
ORIGINAL TITLE BUDGET
0 The Warriors Way 425000000
1 Pirates of the Caribbean: On Stranger Tides 380000000
2 Pirates of the Caribbean: At Worlds End 300000000
3 Avengers: Age of Ultron 280000000
4 Superman Returns 270000000
In [48]:
fig = px.histogram(data_frame=df, x="BUDGET",y="ORIGINAL TITLE",title="Movie Budget",opacity=0.8)
fig.show()
In [ ]: